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syllabus [2016/05/04 11:06]
anderson [Grading]
syllabus [2016/12/14 07:40]
anderson [Textbook]
Line 31: Line 31:
 ===== Textbook ===== ===== Textbook =====
  
-=== Required ===+There are no required text books for this course.  Readings may be assigned from the following on-line books.
  
-[[http://www.cmpe.boun.edu.tr/~ethem/i2ml3e/|Introduction to Machine Learning]]by Ethem Alpaydin3rd editionMIT Press, 2014.+[[http://www.deeplearningbook.org/|Deep Learning]] by Ian GoodfellowYoshua Bengioand Aaron Courville
  
- +[[http://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto2nd edition. On-line and free.
-=== Optional === +
- +
-On-line material is available on the course [[Resources]] web page Other books that may be helpful are listed here.+
  
 [[http://shop.oreilly.com/product/0636920023784.do|Python for Data Analysis]], by Wes Kinney, O'Reilly Media, Inc., 2013. [[http://shop.oreilly.com/product/0636920023784.do|Python for Data Analysis]], by Wes Kinney, O'Reilly Media, Inc., 2013.
  
-[[http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. On-line and free. You can also read the book through Morgan library. Visit [[http://catalog.library.colostate.edu/search~S5?/treinforcement+learning/treinforcement+learning/1%2C12%2C16%2CB/frameset&FF=treinforcement+learning+an+introduction&1%2C%2C3|this page]] and click on the “View electronic book” link. 
  
 ===== Instructors ===== ===== Instructors =====
syllabus.txt · Last modified: 2020/12/06 10:37 by anderson